code: def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--output-wav-path",
          default="./output.wav",
          type=str,
          metavar="PATH",
          help="The path to output the reconstructed wav file.",
      )
      parser.add_argument(
          "--jit",
          default=False,
          action="store_true",
          help="If used, the model and inference function is jitted.",
      )
      parser.add_argument(
          "--no-batch-inference",
          default=False,
          action="store_true",
          help="Don't use batch inference.",
      )
      parser.add_argument(
          "--no-mulaw",
          default=False,
          action="store_true",
          help="Don't use mulaw decoder to decoder the signal.",
      )
      parser.add_argument(
          "--checkpoint-name",
          default="wavernn_10k_epochs_8bits_ljspeech",
          choices=list(_MODEL_CONFIG_AND_URLS.keys()),
          help="Select the WaveRNN checkpoint.",
      )
      parser.add_argument(
          "--batch-timesteps",
          default=100,
          type=int,
          help="The time steps for each batch. Only used when batch inference is used",
      )
      parser.add_argument(
          "--batch-overlap",
          default=5,
          type=int,
          help="The overlapping time steps between batches. Only used when batch inference is used",
      )
      args = parser.parse_args()
      return args
  
file_path: audio/examples/pipeline_wavernn/inference.py
imports: import argparse
  import torch
  import torchaudio
  from processing import NormalizeDB
  from torchaudio.datasets import LJSPEECH
  from torchaudio.models import wavernn
  from torchaudio.models.wavernn import _MODEL_CONFIG_AND_URLS
  from torchaudio.transforms import MelSpectrogram
  from wavernn_inference_wrapper import WaveRNNInferenceWrapper
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args